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Machine learning model to project the impact of COVID-19 on US motor gasoline demand

Author

Listed:
  • Shiqi Ou

    (Energy and Transportation Science Division, Oak Ridge National Laboratory)

  • Xin He

    (Aramco Services Company: Aramco Research Center—Detroit)

  • Weiqi Ji

    (Massachusetts Institute of Technology)

  • Wei Chen

    (Michigan Department of Transportation)

  • Lang Sui

    (Aramco Services Company: Aramco Research Center—Detroit)

  • Yu Gan

    (Energy Systems Division, Argonne National Laboratory)

  • Zifeng Lu

    (Energy Systems Division, Argonne National Laboratory)

  • Zhenhong Lin

    (Energy and Transportation Science Division, Oak Ridge National Laboratory)

  • Sili Deng

    (Massachusetts Institute of Technology)

  • Steven Przesmitzki

    (Aramco Services Company: Aramco Research Center—Detroit)

  • Jessey Bouchard

    (Aramco Services Company: Aramco Research Center—Detroit)

Abstract

Owing to the global lockdowns that resulted from the COVID-19 pandemic, fuel demand plummeted and the price of oil futures went negative in April 2020. Robust fuel demand projections are crucial to economic and energy planning and policy discussions. Here we incorporate pandemic projections and people’s resulting travel and trip activities and fuel usage in a machine-learning-based model to project the US medium-term gasoline demand and study the impact of government intervention. We found that under the reference infection scenario, the US gasoline demand grows slowly after a quick rebound in May, and is unlikely to fully recover prior to October 2020. Under the reference and pessimistic scenario, continual lockdown (no reopening) could worsen the motor gasoline demand temporarily, but it helps the demand recover to a normal level quicker. Under the optimistic infection scenario, gasoline demand will recover close to the non-pandemic level by October 2020.

Suggested Citation

  • Shiqi Ou & Xin He & Weiqi Ji & Wei Chen & Lang Sui & Yu Gan & Zifeng Lu & Zhenhong Lin & Sili Deng & Steven Przesmitzki & Jessey Bouchard, 2020. "Machine learning model to project the impact of COVID-19 on US motor gasoline demand," Nature Energy, Nature, vol. 5(9), pages 666-673, September.
    • Handle: RePEc:nat:natene:v:5:y:2020:i:9:d:10.1038_s41560-020-0662-1
    DOI: 10.1038/s41560-020-0662-1
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    Citations

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    Cited by:

    1. Li, Zheng & Zhou, Bo & Hensher, David A., 2022. "Forecasting automobile gasoline demand in Australia using machine learning-based regression," Energy, Elsevier, vol. 239(PD).
    2. Prakash Chandra Mishra & Anand Gupta & Saikat Samanta & Rihana B. Ishaq & Fuad Khoshnaw, 2022. "Framework for Energy-Averaged Emission Mitigation Technique Adopting Gasoline-Methanol Blend Replacement and Piston Design Exchange," Energies, MDPI, vol. 15(19), pages 1-26, September.
    3. Gao, Yixuan & Malone, Trey & Schaefer, K. Aleks & Myers, Robert J., 2023. "Disentangling Short-Run COVID-19 Price Impact Pathways in the US Corn Market," Journal of Agricultural and Resource Economics, Western Agricultural Economics Association, vol. 48(2), May.
    4. Nikolaos Apostolopoulos & Panagiotis Liargovas & Nikolaos Rodousakis & George Soklis, 2022. "COVID-19 in US Economy: Structural Analysis and Policy Proposals," Sustainability, MDPI, vol. 14(13), pages 1-15, June.
    5. Wang, Qiang & Li, Shuyu & Zhang, Min & Li, Rongrong, 2022. "Impact of COVID-19 pandemic on oil consumption in the United States: A new estimation approach," Energy, Elsevier, vol. 239(PC).
    6. Tayarani N., Mohammad-H., 2021. "Applications of artificial intelligence in battling against covid-19: A literature review," Chaos, Solitons & Fractals, Elsevier, vol. 142(C).
    7. Weihua Lei & Luiz G. A. Alves & Luís A. Nunes Amaral, 2022. "Forecasting the evolution of fast-changing transportation networks using machine learning," Nature Communications, Nature, vol. 13(1), pages 1-12, December.
    8. Francesco Piccialli & Vincenzo Schiano Cola & Fabio Giampaolo & Salvatore Cuomo, 2021. "The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic," Information Systems Frontiers, Springer, vol. 23(6), pages 1467-1497, December.
    9. Bocklet, Johanna, 2020. "The Reformed EU ETS in Times of Economic Crises: the Case of the COVID-19 Pandemic," EWI Working Papers 2020-10, Energiewirtschaftliches Institut an der Universitaet zu Koeln (EWI).
    10. Hanmin Dong & Xiujie Tan & Si Cheng & Yishuang Liu, 2023. "COVID-19, recovery policies and the resilience of EU ETS," Economic Change and Restructuring, Springer, vol. 56(5), pages 2965-2991, October.
    11. Zhang, Xiaokong & Chai, Jian & Tian, Lingyue & Yang, Ying & Zhang, Zhe George & Pan, Yue, 2023. "Forecast and structural characteristics of China's oil product consumption embedded in bottom-line thinking," Energy, Elsevier, vol. 278(PA).
    12. Feng Wang & Min Wu, 2021. "The Impacts of COVID-19 on China’s Economy and Energy in the Context of Trade Protectionism," IJERPH, MDPI, vol. 18(23), pages 1-23, December.
    13. Hui Zhu, 2023. "Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors," Sustainability, MDPI, vol. 15(18), pages 1-19, September.

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